General Purpose set theoretic processor

ABSTRACT

A general purpose processor architecture (methods and apparatuses) that can discern all subsets of a serial data stream which fulfill an arbitrarily complex reference pattern. The invention comprises an ordered set of Detection Cells conditionally interconnected according to the reference pattern and operationally controlling one another&#39;s states through the network. The invention preferably includes a Host Interface to enable reporting of Results from a search session as well as the input and control of reference patterns and source data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.11/353,318, filed on Feb. 13, 2006, entitled “General Purpose SetTheoretic processor” the content of which is incorporated herein byreference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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INCORPORATION BY REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

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COPYRIGHTED MATERIAL

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BACKGROUND OF THE INVENTION

1. Field of the Invention (Technical Field)

The present invention relates to the field of digital electronic devicesfor recognizing specified patterns in a data stream, specificallyclaimed is a general purpose architecture and device for set theoreticprocessing.

2. Description of Related Art

In the pattern recognition field, whether in searching textual data orother form of source data, four problems persist, notably acuity,throughput, scalability and cost. Accordingly, these are the measures ofeffectiveness for any new technology in this field.

Acuity is measured as the sum of False Positives and False Negatives inthe results of a scan. False positives occur when the scan results inirrelevant patterns. False Negatives occur when the scan fails toidentify patterns that are, in fact, relevant. The ideal patternrecognition device will not make such errors. The next best device willenable a user to control the amount of error as well as the ratio ofFalse Positives to False Negatives and to trade off acuity vs.throughput and/or cost for each user situation.

As to scalability, pattern matching involves identifying symbols andsyntax identical to the user's expression of interest. Patternrecognition goes further by enabling the specification of a class ofequivalent terms and by aggregating all qualifying instances. Thisdistinction became important as search technology progressed from textsearch to imagery search to detection of malware for cybersecurity andfor detection of evolving patterns in biocomplexity informatics.Accordingly, minimizing False Positives and False Negatives may entailreference patterns that specify upwards of 100 terms each averaging 10features. The vaunted Google search engine experienced an average queryof 2.3 terms, circa 2004. Modern recognition devices should have thecapacity to compare upwards of 1000 features to the source data streamand be scalable to 1 million features. Other dimensions of scalabilityinclude implementation of a personal-scale device to a federated systemof millions of devices throughout the World Wide Web.

Throughput is the speed at which the source data is examined and resultsreported. The typical measure of throughput is characters per second.Constant or at least predictable throughput is best, not dependent onthe complexity of the reference pattern nor the number of recognitionsper unit time.

Cost is the total cost of ownership of a search episode regardless ofhow the costs are allocated. For example, a user pays nothing for aGoogle search episode but somebody is paying for the MIPS (millions ofinstructions per second), bytes and baud that are used to accomplish thesearch. Cost includes the preprocessing of source data as well as theactual search episode.

Starting approximately in the mid-1900's, various strategies have soughtto improve performance in one or more of the measures by leveragingtechnologies and innovating architectures. The state of the art is stillfar from realizing, simultaneously, specifiable acuity, throughput atthe speed of silicon, scale from single user to federated users and costlevels that small and medium enterprises and even individuals canafford.

The following paragraphs summarize the field to date.

The general purpose computer, designed to perform arithmetic, has beenused since the 1950's for comparison of digital data in the form ofcharacters, character strings and combinations of character strings. Astraightforward program can utilize the memory and CPU (centralprocessing unit) to input a reference character then compare it,sequentially, to multiple characters of various kinds as presented by asource data stream. The presence in the source data stream of a matchingcharacter can be flagged for subsequent reference. More than one suchcharacter may be found and flagged and more than one instance of anyspecific character may be found and flagged. Each such operationconsumes several clock cycles of a modern microprocessor.

If the reference is a string consisting of multiple characters in aspecific sequence, i.e., representing a word of text, or a music melodyor a genomic pattern, then a more complicated program is required.Character-level comparison proceeds as before then the interim resultsare stored for subsequent processing to determine whether the charactersthat qualified are in the sequence required for a word-level match. Thisis known as the combinatorial explosion problem because the number ofmachine cycles increases as the square of the number of conditionalmatches. Such operations typically consume thousands of clock cycles andthere is no upper limit.

If the reference consists of a phrase, e.g., a string of words in aspecific order, then the program, using recursion, is only somewhat morecomplicated but the combinatorial explosion can become even moredramatic and can consume billions of clock cycles. Although gridconfigurations of megaflop processors can supply the clock cycles theexpense of the device quickly becomes prohibitive and throughput can bein the range of only a few characters per second.

The dismal acuity exhibited by pattern matching machines to date stemsfrom attempts to avoid the combinatorial explosion problem. Most textapplications use key words to surrogate the source data. Then searchescompare the reference pattern to only the key word file, not to theactual text. This approach invokes the well known problem of retrievingcitations that are irrelevant (false positives) thus incurring waste andcost. A not so well known but worse outcome is that are truly relevantpatterns in the data being scanned are not recognized (false negatives)because the content was not represented with sufficient fidelity by thekey words used.

Attempts to extract meanings from text are frustrated by the complexityof the problem but more so by the indexing to terms as noted in theprevious paragraph. Implementations of pattern recognition have beendemonstrated with pre-processing and post-processing software, such asstatistical clustering and Latent Semantic Indexing. In essence theseseek to overcome the limitations inherent in indexed or censored textstreams by further processing of the term matches found by the hardware.This approach to recovery of meanings in the censored text can neverovercome the limitations imposed by the censoring in the first place.

-   -   United States Patent Publication 2005/0154802, Parallel pattern        detection engine. Multiple processing units (PUs) customized to        do various modes of pattern recognition. Each pattern has an        Opcode e. PUs may be cascaded to enable longer patterns to be        matched or to allow more patterns to be processed in parallel        for a particular input data stream. Cost and throughput are        highly suspect. Also, the application appears to implement        nesting but not equivalence classes.

SIMM, SIMD and similar hardware CPU embellishments have been inventedfor data flow applications. These have demonstrated speed improvementsin the single digit range but also increased cost.

-   -   United States Patent Publication 2005/0257025, State engine for        data processor. Uses parallel processors, such as SIMD array        processors. Claims that a read/modify/write operation can be        performed in only two cycles and a complete command in only        three to five cycles. These performances appear to be for        fundamental pattern matching but not for partial matches and        consideration of variants such as plurals.

Supercomputer configurations and more recently grid configurations ofmicroprocessors have been programmed for set theoretic processing bothfor Very Large Data Base situations and genomic research. Cost inhibitsmost potential users from this option.

In the limit, the basic Von Neuman stored program computer simply cannotexhibit the speed/cost ratios available with other implementations.

The special purpose processor category contains many examples of priorart. Early examples include the General Electric series;

-   -   U.S. Pat. No. 3,358,270, December 1967.    -   U.S. Pat. No. 4,094,001, Digital logic circuits for comparing        ordered character strings of variable length, Jun. 6, 1978.    -   U.S. Pat. No. 4,451,901. High speed search system. May 29, 1984.        Also in this category are the TRW series;    -   U.S. Pat. No. 5,051,947, High-speed single-pass textual search        processor for locating exact and inexact matches of a search        pattern in a textual stream, Jun. 6, 1978.    -   U.S. Pat. No. 4,760,523, Fast search processor, Jul. 26, 1988.

Being complex logic devices, special purpose processors are not onlyexpensive to produce (product cost exceeding $10,000) but also subjectto failure rates that frustrated operational users. Further, the designswere neither scalable nor extensible. The method of performingcharacter/character set comparisons limited them to pattern matchingrather than pattern recognition.

-   -   U.S. Pat. No. 4,747,072, Pattern addressable memory, May        24, 1988. Performance shortfall from rule based active        construction of variable content in key words. Does not        accommodate equivalence classes.    -   United States Patent Publication 2003/0055799, Self-organizing        data driven learning hardware with local interconnections. Does        not handle equivalence classes. Throughput shortfall.    -   U.S. Pat. No. 4,531,201. Patterns limited in length to size of        shift registers.    -   U.S. Pat. No. 4,625,295. Can handle 16-bit characters at loss of        throughput. Length of shift registers limits length of words.        Cannot detect Kleen Closures. Decoder requires a delay to decode        a character. Does not handle equivalence classes.

Being ever more complicated, logic devices using parallelism forthroughput in various string pattern matching scenarios each exhibitedcost limitations were limited in scalability as well. Also, preciseinternal timing of the logic circuits made it nearly impossible tore-implement them as semiconductor technology advanced.

Associative Memories and Contents Addressable Memories have been used toreduce the number of clock cycles required to fetch data into registers.While improving performance these approaches do not reduce costs andnone to date have yielded significant improvements regarding the acuitymeasure of effectiveness.

-   -   United States Patent Publication 2004/0123071, Cellular engine        for a data processing system. Matching device. Does not support        Boolean, semantic or set theory logic.    -   United States Patent Publication 2004/0080973, Associative        memory, method for searching the same, network device, and        network system. Associative memory carries out a search        operation in plural fields. Does not appear to support complex        reference patterns. Three memories, three cycles indicates        shortfalls in throughput and acuity    -   United States Patent Publication 2003/0229636, Pattern matching        and pattern recognition system, associative memory apparatus,        and pattern matching and pattern recognition processing method.        Word-level, multiple cycles.    -   United States Patent Publication 2003/0014240, Associative        memory device with optimized occupation, particularly for the        recognition of words. Spreadsheet approach, relationships among        classes not supported. Logic limited,    -   United States Patent Publication 2004/0250013, Associative        memory system, network device, and network system. Includes        Reset to find second instance of a pattern. Does not support        equivalence classes.

Neural Net based recognizers have been used for string pattern matchingin order to implement rapidly adaptive reference patterns. These haveproven effective in specific applications such as email spam signatureidentification and adaptive tracking but do not exhibit the speed andperformance for general application.

-   -   United States Patent Publication 2005/0049984, Neural networks        and neural memory. Does not support equivalence classes.    -   United States Patent Publication 2002/0059152, Neural processing        module with input architectures that make maximal use of a        weighted synapse array. Symbol syntax but not semantics.    -   United States Patent Publication 2002/0032670, Neural network        processing system using semiconductor memories. Aggregations are        linear.

Application-specific devices have been devised for pattern matching of2D and 3D images, but do not have the Boolean, semantic and set theorylogic.

-   -   United States Patent Publication 2002/0125500, Semiconductor        associative memory. Emphasizes processing versus use of memory        and systolic operations. Shortfall in throughput and cost.    -   United States Patent Publication 2002/0168100, Spatial image        processor. Assumes location implicit relationships in data        stream (e.g., pixels).    -   United States Patent Publication 2003/0194124, Massive training        artificial neural network (MTANN) for detecting abnormalities in        medical images. Uses sequential stored program.    -   United States Patent Publication 2004/0156546, Method and        apparatus for image processing. Defines three categories of        processing, object-independent processing a plurality of        processors each of which is associated with a different one of        the pixels of the image, object-dependent processing using a        symmetric multi-processor. The plurality of processors may form        a massively parallel processor of a systolic array type and        configured as a single-instruction multiple-data system, and        object composition, recognition and association, using a unified        and symmetric processing of N dimensions in space and one        dimension in time. The plurality of processors is formed on a        semiconductor substrate different from the semiconductor        substrate on which images are captured.

Systolic Arrays use pulse propagation through preformed switchingnetworks to parallelize logical relationships without resorting to thevon Neumann paradigm. These are much faster and less expensive thansequential processors. Inventions and embodiments to date have beenapplication specific and have not allowed sufficiently quickreconfigurations of the switching network.

The more particularized field of the present invention started withmachines that detected Match or No Match, one term at a time. Next camefull Boolean operators across collections of terms. Then the addition ofDon't Care logic to the Match, No Match choices allowed detection ofpartial matches. Delimiters enabled detection of syntactic clues such asend of word, end of sentence, end of paragraph, end of section, end offile, end of record, etc. Next came detecting strings of terms (e.g.,phrases). Throughout was the presumption that the search would becomposed and expressed by humans in the form of queries. Search enginesor adjunct software did not greatly aid how humans formulated queries.Linguists created very sophisticated preprocessing and post processingsoftware but these did not make significant improvements in acuity,throughput, scalability or cost, let alone improving all at the sametime. The field of artificial intelligence paralleled the search enginefield but without significant cross-disciplinary sharing.

The field started with machines that detected Match or No Match, oneterm at a time. Next came full Boolean operators across collections ofterms. Then the addition of Don't Care logic to the Match and No Matchchoices allowed detection of partial matches. Delimiters enableddetection of syntactic clues such as end of word, end of sentence, endof paragraph, end of section, end of file, end of record, etc. Next camethe capability to detect strings of terms (e.g. phrases).

Throughout was the presumption that the search would be composed andexpressed by humans in the form of queries. Search engines or adjunctsoftware did not greatly aid how humans formulated queries. Meanwhile,linguists created very sophisticated preprocessing and post processingsoftware but these did not make significant improvements in acuity,throughput, scalability or cost, let alone improving all at the sametime.

Many of the advancements were paced by Moore's price/performance law inthe semiconductor field. The field of artificial intelligence paralleledthe search engine field but unfortunately without significantcross-disciplinary sharing. The advent of knowledge management in the1990's made many more people aware of lexicons and taxonomies, the[then] means of expressing the relationships among entities in additionto describing the entities. The advent of semantic web development in2001 intended to enable computers to interchange data based on themeaning of the data instead of just on its location in a format hasfostered expressions of knowledge models as formal ontologies.

Set theory has proven useful for making assertions about relationships.The advent of digital image patterns search has advanced its useconsiderably. Currently, set theoretic expressions are as prevalent asBoolean logic and algorithmic operators. This has prompted distinctlynew machine architectures featuring systolic arrays and dataflow-facilitation examples. Now interest increasing from facilitation ofcomputer system data interchanges to facilitation of human knowledgeinterchanges and to facilitation of interchanges among diverse,distributed systems of humans. Applying digital devices to thedisambiguation of human communications is the next wave in this field.The astounding complexity of this challenge motivates development of ageneral purpose machine that can execute a variety of such expressions.The present invention provides such a method and apparatus.

In order to locate all of the data objects relevant to a given referentand only those objects it is necessary to overcome the heterogeneity ofthe subject data. Heterogeneity exists on two levels. The first levelfor digital text is transcription variation such as differences inspelling, spelling errors, typographical errors, punctuationdifferences, spacing variation, the presence of “special” bytes used tocontrol the display or transmission medium but which themselves carry nomeaning, and recently, obfuscation characters intended to spoof spamdetectors. A popular example of the latter is:

-   -   “Aoccdrnig to a rscheearch at Cmabrigde Uinervtisy, it deosn't        mttaer in waht oredr the Itteers in a wrod are, the olny        iprmoetnt tihng is taht the frist and Isat Itteer be at the        rghit pclae. The rset can be a total mses and you can sitll raed        it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not        raed ervey Iteter by istlef, but the wrod as a wlohe.”

Cognates in other types of digital data are, for example: DNA spellingerrors, Background noise, and Varying pronunciation.

The second level of heterogeneity is the semantic level. Humans aregifted inventors of different ways of expressing the same idea. Thismeans that for every component of a referent many variations may bepossible (varying sentence and paragraph structuring, and many figuresof speech (synonyms, allegory, allusion, ambiguity, analogy, eponym,hyperbole, icon, index, irony, map, metaphor, metonym, polysemousmeaning, pun, sarcasm, sardony, sign, simile, synecdoche, symbol, token,trope) and class, subclass, idioms, and super class words andexpressions).

Accordingly, rather than looking for the occurrences of a word orphrase, or even a few words or phrases, in a body of text, Ashby's Lawof Requisite Variety (for appropriate regulation the variety in theregulator must be equal to or greater than the variety in the systembeing regulated) demands a way of finding all of the expressionsequivalent to a set of referents. A responsive machine must be able toprocess set theoretic operators as well as Boolean operators andsemantic operators such as precedence and aggregation. In this documentthe set of strings equivalent to a referent is called an equivalenceclass. A description of the members of an equivalence class is called aReferent Pattern.

Acuity is achieved by providing a means to scan a data stream forcontent fulfilling Reference Patterns of sufficient selectivity andsensitivity to perceive just the digital objects of interest.Simultaneously, throughput, scalability and cost must be achieved aswell.

BRIEF SUMMARY OF THE INVENTION

The present invention is of a general purpose, set theoretic processor(and concomitant method) comprising: a recognition network comprising astate-change routing matrix, an ordered plurality of detection cells,and a reaction memory; a pattern memory; an aggregation networkcomprising an aggregation routing matrix and a threshold logic; and alocal clock and sequencer. In the preferred embodiment, the processoroperates in modes of operation comprising load mode, scan mode, and testand diagnostic mode. The state-change routing matrix provides areticulum of latent connections from any set of source detection cellsto any set of successor detection cells. A reference pattern establishesactual pathways through the state-change routing matrix from source tosuccessor cells. The plurality of detection cells comprises 1024 or morecells, wherein each of the detection cells is associated with one columnof the pattern memory, each of the detection cells is a finite statemachine having two states, and the latent reaction of each of thedetection cells to any given input value is specified by the contents ofthe reaction memory and whose manifest reaction is determined by thecell's current state acting on its latent reaction. The pattern memoryis conventionally addressed in load mode and test and diagnostic modewith one word of at least 1024 bits being written to or read from amultiplexer/de-multiplexer at a time; and in scan mode four words of atleast 1024 bits each are accessed in parallel to generate in paralleltwo results words (detection cell response and next state) of at least1024 bits each. Reactions of all detection cells are determinedsimultaneously, in parallel. The threshold logic comprises a pluralityof group threshold logic cells, wherein the aggregation routing matrixprovides potential connections between each the detection cell and eachgroup threshold logic cell, a reference pattern establishes actualconnections between detection cells and group threshold logic cells, areference pattern determines whether an output from the detection cellit connects to a group threshold logic cell will be transmitted to thegroup threshold logic cell on every occurrence or only once, the groupthreshold logic cells are initialized at the beginning of a scan mode,and each of the group threshold logic cells has a 1 bit output. Allprocessing is accomplished on source data provided to the processor inone systolic cycle per source data input. The processor recognizesstrings in source data that fulfill a reference pattern specification ofa set of fixed and variable component substrings comprising one or moreof fixed strings of primary input components, strings in which relativelocations can have one of a number of different values, strings in whicha value or set of values can repeat zero or more times, and strings inwhich one or more substrings can have one of a number of alternativevalues. The source data is any of the following: nucleotide sequences;amino acid sequences; speech pattern sequences; lexicographic sequences;signal analysis sequences including electromagnetic, optical, acousticor seismic data sequences; sequences derived from a graphic imageincluding x-ray images, CAT scan images, MRI images, television images,fingerprint images, and photographic images; pixel location data; lawenforcement related sequence data including fingerprint data, voiceprintdata, and genetic profile data; and sequence data comprising geneexpression profiles.

Bandwidth is necessary for both acuity and throughput. A primary objectof the invention is to provide a 10-fold to 100-fold advancement withrespect to current architectures if implemented in the same technology.A principal approach is to trade space for logic by using a randomaccess memory with minimal added logic and control functions.

Another object of the invention is to minimize process steps, usingsystolic techniques wherever possible, and to trade space for logic byusing random access memory with minimal logic and control functions.

A further object of the invention is to provide an agile architecturefeaturing a framework and module arrangement that can accommodateexpansion or contraction of throughput, repertoire and level of languagewithout affecting existing capabilities and behaviors.

Other objects of the invention include:

-   -   A combination of a Recognition Network and an Aggregation        Network that can achieve significant improvements in information        extraction acuity.    -   Use of a reference pattern (description of patterns whose        presence in a data entity indicates a high probability that that        entity is relevant to a subject of interest) as the basis for        the microcode (state table, initial state values, switch        settings, and logic thresholds) needed to program the two above        mentioned networks.    -   Construction of the above noted Networks within a single        semiconductor device, which can then be used to exploit data        paths orders of magnitude greater than achievable across        multiple semiconductor devices and processing speeds not        achievable across multiple semiconductor devices.    -   Use of the above networks for extraction of data by reference        patterns sufficiently complex to distinguish relevant data from        non-relevant data to an acuity not achievable by existing        methods at comparable speed performance.    -   Use of the above networks to improve extraction acuity without        degradation of speed as the query becomes more complex to        achieve improved acuity.    -   A network that does not suffer degradation of speed depending on        the number of hits that are found within the data being        searched.

Advantages of the invention include: (1) Enabling user control of acuitythrough choices about the extent and content of the reference pattern(RP); (2) Enabling user confidence in results by providing a deviceintegrity check that may be run at any time; (3) Enabling userinvocation of not only variations on a reference word (e.g., pluralforms) but also invocation of equivalent words and phrases (e.g., cat,feline, Tabby); the preferred embodiment is 1024 cells ) but up to onemillion cells are consistent with VLSI implementation media circa 2005.

The invention provides fungibility advantages, with a cost low enough tohave this device beside every CPU and every Read Head thus leveragingMetcalf's Law. There is a low cost of ownership through a combination oflow cost of device, configurability as a co-processor in a generalpurpose computing system. Thanks to computer preprocessing of sourcedata not being required, adjunct software complexity is minimized, andresults in one systolic cycle per input.

High scalability accommodates the span of reference patterns and volumesof data to be searched. An objective is a 10-fold relaxation of limitson span and volume over current technology. A principal approach is toabstract the semantic and set theory logic and unify them in a highlyparallel, extensible architecture.

Other objects, advantages and novel features, and further scope ofapplicability of the present invention will be set forth in part in thedetailed description to follow, taken in conjunction with theaccompanying drawings, and in part will become apparent to those skilledin the art upon examination of the following, or may be learned bypractice of the invention. The objects and advantages of the inventionmay be realized and attained by means of the instrumentalities andcombinations particularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated into and form a partof the specification, illustrate one or more embodiments of the presentinvention and, together with the description, serve to explain theprinciples of the invention. The drawings are only for the purpose ofillustrating one or more preferred embodiments of the invention and arenot to be construed as limiting the invention, and include the referencenumerals described below. In the drawings:

FIG. 1 is a block diagram of the present invention in a representativeconfiguration;

FIG. 2 is a block diagram showing preferred GPSTP components;

FIG. 3 is a block diagram of GPSTP Conventional Memory Structure, Loadmode;

FIG. 4 is a block diagram of GPSTP alternative memory structure, Scanmode;

FIG. 5 is a block diagram showing Recognition Network components;

FIG. 6 is a functional diagram explaining the Counter Representationmethod;

FIG. 7 is a functional diagram explaining the Detection Propagationmethod;

FIG. 8 is an interface diagram showing the Detection Cell formulaicdescription;

FIG. 9 is a block diagram showing the Complex Term method;

FIG. 10 is a functional diagram of the State-Change Routing Cellformulaic description;

FIG. 11 is a block diagram showing the Neighbor Enable method;

FIG. 12 is a functional diagram of examples of State-Change RoutingMatrix methods;

FIG. 13 is a block diagram showing the “Bridge” a Flawed DetectionColumn method;

FIG. 14 is a block diagram of the Aggregation Network;

FIG. 15 is a block diagram showing the Aggregation Routing Matrix;

FIG. 16 is an interface diagram of the Aggregation Routing CellFormulaic Description;

FIG. 17 is a block diagram of the Threshold Logic;

FIG. 18 is a schematic diagram of Detection Cell Detail;

FIG. 19 is a schematic diagram of State-Change Routing Cell Detail;

FIG. 20 is a schematic diagram of Aggregation Routing Cell Detail; and

FIG. 21 is a schematic diagram of Group Threshold Logic Detail.

INDEX OF REFERENCE NUMERALS

Reference Numeral Named Item Figure # 1. Set Theoretic Processor 1 2.Host Device Interface 1 3. Reference Pattern 1 4. Processor InternalStates 1 5. Data Stream (input byte b as address) 1 6. Results 1 7.Composite Boolean Logic 1 8. Results From CBL 1 9. Control Status 1 10.Row Address Decoder 2 11. 32-1024 Multiplexer/De-multiplexer 2 12.Recognition Network 2 13. Sequencer 2 14. Pattern Memory 2 15.Aggregation Network 2 16. 10 bit Pattern Memory row address 3 17.Pattern Memory Selected Row (R/W) 3 18. Row Address Decoder Control 319. Pattern Memory Control 3 20. Multiplexer/De-multiplexer Control 321. 1024 bit Pattern Memory word 3 22. 8 bit Selected Reaction TripleRows (MR_(b), MS_(b), 4 MA_(b)) 23. Composite Boolean Logic Control 424. Response Result (Si {circumflex over ( )} MR_(i,b)) for DC_(i) 4 25.Reaction Memory 5 26. MA_(b,i) - Auto-Enable bit for input byte b andDC_(i) 5 27. MR_(b,i) - Response bit for input byte b and DC_(i) 5 28.MS_(b,i) - Successor-Enable bit for input byte b and 5 DC_(i) 29.DC_(i) - Detection Cell i 5 30. Successor-Enable output (Si {circumflexover ( )} MS_(i,b)) for DC_(i) 5 31. Successor-Enable for DC_(i) fromits Precursors 5 32. State-Change Routing Matrix 5 33. C the clocksignal from the sequencer 5 34. INI - Initiate Command 8 35. IS_(i)initial state value for DC_(i) 8 36. Reverse Propagation from SRC_(j,i)10 37. Forward Propagation to SRC_(j,i) 10 38. Enable Disjunctive “Sum”into SRC_(j,i) 10 39. State-Change Routing Cell j, i 10 40. ForwardPropagation from SRC_(j,i) 10 41. Reverse Propagation to SRC_(j,i) 1042. Enable Disjunctive “Sum” from SRC_(j,i) 10 43. Aggregation RoutingMatrix 14 44. Group Logic Lines 14 45. Threshold Logic 14 46.Aggregation Routing Cells 15 47. Group Logic Line entering ARC_(m,n) 1548. Group Logic Line exiting ARC_(m,n) 15 49. Group Threshold Logic_(j)17 50. TL_(i) Threshold Logic Output for GTL_(i) 17 51. Auto-Enableoutput (Si {circumflex over ( )} MA_(i,b)) for DC_(i) 18 52. Write thestate S_(i) for DC_(i) control line 18 53. Next State S′_(i) for DC_(i)18 54. S_(i) the current state of DC_(i) 18 55. Accept ReversePropagation 19 56. Accept Forward Propagation 19 57. Forward PropagationSwitch 19 58. Reverse Propagation Switch 19 59. Select ReversePropagation 19 60. Select Forward Propagation 19 61. Select Logic Groupj for DC_(i) 20 62. Not (Select Logic Group j for DCR Latch Bit) 20 63.Latch-No-Latch for Logic Group j × DC_(i) 20 64. Not(Response Latch bitfor Logic Group j × DC_(i)) 20 65. Response Latch bit for Logic Group j× DC_(i) 20 66. Threshold Logic Counter i 21 67. Threshold Initial Value21

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to the field of digital electronic devicesfor recognizing specified patterns in a data stream specifically claimedis a general purpose architecture and device for set theoreticprocessing. This type of processing arises in many different fields ofendeavor, but can be understood more easily in terms of a search of analphanumeric data base, not to locate all the occurrences of aparticular word or phrase, but to locate all of the data objects(documents, images, DNA base sequences, audio clips) relevant to a givenissue (referent) and only those data objects.

As shown in the drawings for purposes of illustration, the presentinvention is of a high bandwidth general purpose set theoretic processor(GPSTP) capable of perceiving complex patterns in a digital symbolstream at data rates orders of magnitude faster than conventionalcomputers implemented in the same semiconductor technology (feature sizeand gate speed). The processor is capable of implementation on a singlesemiconductor chip. The processor is unaffected by the meaning of thesymbols provided that the Reference Pattern is formulated assuming thesame symbol set as that of the symbol stream. The processor will performequally well with text (without regard to code- ASCII, EBCDIC, BCD),gray-scale pixel values, DNA sequences, digital audio, or any otherdigital information.

The following definitions are used in descriptions below, but numericalvalues are not intended to be limiting (e.g., the number of bits in thecolumns and rows of Pattern Memory):

GLOSSARY

Vocabulary Term or Symbol Meaning

(A{circumflex over ( )}B) Binary conjunctive (AND) operator if A and Bare one bit binary values, A{circumflex over ( )}B = 1 if and only if A= 1 and B = 1.

(A

B) Binary disjunctive OR operator if A and B are one bit binary values,A{circumflex over ( )}B = 1 if either A = 1 or B = 1. ∩ (A∩B) Binaryconjunctive operator for multiple bit arguments. If A = (A₁, A₂, . . .A_(n)) and B = (B₁, B₂, . . . B_(n)) are n-bit binary “words” A ∩ B =(A₁{circumflex over ( )}B₁, A₂{circumflex over ( )}B₂, . . .A_(n){circumflex over ( )}B_(n)). ∪ (A ∪ B) Binary disjunctive operatorfor multiple bit arguments. If A = (A₁, A₂, . . . A_(n)) and B = (B₁,B₂, . . . B_(n)) are n-bit binary “words” A ∪ B = (A₁∩B₁, A₂ ∩B₂, . . .A_(n) ∩B_(n)). ∩ (A₁, A₂, . . . A_(n)) Conjunctive summation. ∪(A₁, A₂,. . . A_(n)) = 1 if all Ai = 1 (1 ≦ i ≦ n). ∪(A₁, A₂, . . . A_(n))Disjunctive summation. ∪(A₁, A₂, . . . A_(n)) = 1 if any Ai = 1 (1 ≦ i ≦n). Aggregation Network A plurality of switches that map the outputs ofDetection Cells to threshold logic elements. Byte Eight bits. Can betreated as a binary numbers having values 0 to 255 or as a symbol.Auto-Enable Bit A bit in the auto-enable memory (see MA); A bit thataffects the next state of the Detection cell with which it is associatedDetection (of input byte “b” by Detection Cell The state of affairs inwhich b is the current input DCi) byte, DC_(i) is in the “ON” state andone bit of its Reaction Triple addressed by b is 1. The state of affairsin which S_(i) = 1 and [MA_(b,i) = 1, MS_(b,i) = 1, or MR_(b,i) = 1]Detection Cell (DC_(i)) The fundamental detection unit of the presentinvention. A programmable two state (“On” or “OFF”, equivalently“Enabled” or “Disabled”) deterministic finite state automaton configuredby a Detection Specification to detect a specific set of byte stringsDetection Specification See Feature Enabling Propagation Path (fromFeature F_(n) to A set of switches in State-Change Routing MatrixFeature F_(m)) cells that allow F_(n) to enable F_(m), (n ≠ m). FeatureThe set of 256 Reaction Triples in a Detection Cell's Reaction Memorythat determine the cell's behavior. Memory, Auto-Enable (MA) The 256 ×1024 bit memory of bits that affect a detection cell's own next state.MA_(b,i) designates the auto-enable bit corresponding to input byte “b”for Detection Cell DC_(i.) Memory, Control (MC) The 256 × 1024 bitmemory of state and switch settings. Memory, Response (MR) The 256 ×1024 bit memory of response bits. MR_(b,i) designates the response bitcorresponding to input byte “b” for Detection Cell DC_(i.) Memory,Successor-Enable (MS) The 256 × 1024 bit memory in which successorenable bits are stored. MS_(b,i) designates the Successor-Enable bitcorresponding to input byte “b” and Detection Cell DC_(i.) PatternMemory The 1024 × 1024 bit random access memory in which a referencepattern is stored. The pattern memory can be thought of as a controlstore and its contents as a micro program. Pattern Memory Row PM_(r) The1024 bit “word” located at Pattern Memory row address r. The currentstate occupies PM₀, for example. Precursor (of a feature F_(m)) AFeature F_(n) (m ≠ n) is a Precursor of F_(m) if it has at least oneSuccessor-Enable bit set to “1” ≡ “True” and there is an EnablingPropagation Path from F_(n) to F_(m.) Reaction (of a Detection Cell DCito an input The Reaction Triple RM_(b,i) masked by the current byte b)state S. (Si{circumflex over ( )}MR_(b,i), Si{circumflex over( )}MS_(b,i), Si{circumflex over ( )}MA_(b,i)) Reaction Memory (RM)Memories MA, MS, MR taken together; RM₀, RM₁, . . . , RM₁₀₂₄. ReactionMemory_(i) for DC_(i) (RM_(i)) The three 256 bit memories MA_(i),MS_(i), MR_(i), corresponding to DC_(i). The Reaction Triplecorresponding to DC_(i). Reaction Triple (RM_(b,i)) For a given bytefrom the source data the three bits MR_(b,i), MS_(b,i), MA_(b,i) storedat row address “b” in RM_(i) Recognition Network A 2¹⁰²⁴ statedeterministic finite state automaton that accepts source data anddetermines the presence or absence of meaningful content as recognizedby one or more Yerms executed in parallel. The totality of DetectionCells together with the Reaction Memory, and State-Change Routing MatrixReference Pattern A description (generalized according to the presentinvention) of a referent that the invention uses to recognize members ofthe referent's equivalence class; The totality of bits in the PatternMemory. Referent An expression of topical content of interest to aperson using the invention. For example, a sequence of DNA bases for aspecific gene, or an English phrase. Referent Equivalence Class Thesubset of digital expressions in a body of source data equivalent to areferent relative to an ontology: for example the set of variants of thereferent gene in a collection of genomes that differ from the referentgene by missing or additional bases, substituted bases, reversedneighbor bases, etc. Response Bit One of 256 bits in the pattern memoryfor a given Detection Cell that causes an output from the cell when thecell is in the “ON” state and receives an input byte whose binary valueequals the address of the bit. For input byte b the Response Bit forDC_(i), is denoted MR_(b,i). Response Result The 1024 bits S ∩ MR_(b)passed from the Recognition Network to the Aggregation Network at clocksignal C. It tells the Aggregation Network which Terms have beensatisfied for the current input byte b. If the n^(th) bitS_(n){circumflex over ( )}MR_(b,n) = 1, the term ending in DC_(n) hasbeen recognized for the current input byte. Scan Pass a stream of bytesto the invention. The term “search” is sometimes used when the source isa data store or archive. But Scan will be used here throughout becausethe nature of the data source has no effect on the invention. State (S)of the Recognition Network The totality of the states S_(i) of theDetection Cells. S = (S₀, S_(i), . . . S₁₀₂₃) State (S_(i)) of aDetection Cell (DC_(i)) A one bit memory whose value “On” ≡ 1 or “OFF” ≡0 is the state of DC_(i). S_(i) is coincident with PM_(0,i) State-ChangeRouting Matrix A matrix that connects Precursor Detection Cells toSuccessor Detection Cells. Term (Complex) A set of (alternative) TermPrefixes (prefix set) followed by a set of one or more alternative TermInfixes (infix set), followed by a set of one or more alternative Termsuffixes (suffix set) where every infix has at least one precursor inthe in the prefix set and every suffix has at least one precursor ineither the prefix or infix sets. Term (Simple) A Term Prefix followedimmediately by a Term Infix followed immediately by a Term Suffix. TermInfix A contiguous set of features having at least one Successor-Enablebit set to“1” ≡ “True” and at least one Precursor. Term Prefix Acontiguous set of features having at least one Successor-Enable bit setto “1” ≡ “True”, beginning with a feature that is initialized “On” andconfigured to remain “On” (that is, with all of its self-enable bitsinitialized “ON”. Term Suffix A contiguous set of features each (exceptthe last) having at least one Successor-Enable bit set to“1” ≡ “True”,having at least one Precursor and terminated by a feature with at leastone “response bit” set to “1” ≡ “True”Overview

In order to locate all of the data objects relevant to a given referentand only those objects it is necessary to overcome the heterogeneity ofthe source data. Heterogeneity exists on two levels. The first level inthe case of digital text is transcription variation:

-   -   Differences in spelling,    -   Spelling errors,    -   Typographical errors,    -   Punctuation differences,    -   Spacing variation,    -   The presence of “special” bytes used to control the display or        transmission medium but which themselves carry no meaning.        Cognates in digital data other than text are, for example:    -   Lighting variations,    -   DNA spelling errors,    -   Background noise, and    -   Varying pronunciation.

The second level of homogeneity is the semantic level. Humans are giftedinventors of different ways of expressing the same idea. This means thatfor every component of a referent many variations may be possible(varying sentence and paragraph structuring, and many figures of speech(synonyms, allegory, allusion, ambiguity, analogy, eponym, hyperbole,icon, index, irony, map, metaphor, metonym, polysemous meaning, pun,sarcasm, sardony, sign, simile, synecdoche, symbol, token, trope) andclass, subclass, idioms, and super class words and expressions).

Accordingly, rather than looking for the occurrences of a word orphrase, or even a few words or phrases, in a body of text, theaforementioned acuity objective demands a way of finding all of theexpressions equivalent to a set of referents The set of stringsequivalent to a referent is called an equivalence class. A specificationof the members of an equivalence class is called a Reference Pattern.

The purposes of the present invention are; to provide a means to scan adata stream for content qualified by Reference Patterns, to enable usersto specify referents of sufficient selectivity and sensitivity toperceive just the digital documents and other digital objects ofinterest, to provide the bandwidth to do so at useful speeds, and tominimize the cost of a device that satisfies all these.

The invention takes a generalized approach. Rather than dealing withcharacter variation for spelling differences, extra characters, missingcharacters, variable numbers of repeated characters; it employs a (byte)with that can be configured to deal any one or combination of thesechallenges as special cases. The use of “byte” in the previous sentencerather than “character” is deliberate and important to understanding theinvention. A byte value is a digital object consisting of eight binarydigits (represented variously as open and closed switches, high voltagelow voltage, etc.) interpreted as zeros and ones, “ON” and “OF”, “True”and “False”, “Yes” and “NO”. The eight bits taken together as a byte areinterpreted as a binary number with decimal equivalent between 0 and255, as a symbol for a character using a particular coding scheme(anciently called a sort sequence as a legacy of punched card sorters),as a value in a gray scale, a sound, a musical note. The inventionoperates on digital objects (for the embodiment being discussed, bytes).It is neutral with respect to what the digital objects represent. It isalso generalized with respect to the number of bits in a digital object;a different embodiment using the same principles and structure can berealized for two bit objects (two bits are all that are needed torepresent the four bases—A, C, G, T—in DNA sequences, e.g.).

The Detection Cell is a deterministic finite state machine. Its behavioris determined by the current byte in the input stream, by its currentstate, and by the contents of its reaction memory (state transition andoutput tables). It has two possible current states “ON” and “OFF”(“Enabled” and “Disabled” will be used as alternate words for states and“Enable” and “Disable” for “Turn ON” and “Turn Off” or “set to the “ON”state, etc., to avoid clumsy construction.). A Detection Cell's ReactionMemory has a location for each possible byte value. It is from thisproperty that generality with respect to byte interpretation andtranscription variation derives. With respect to interpretation, aDetection Cell's Reaction Memory entry for a given byte value depends onthe meaning of that byte value in the coding scheme of the input. Todetect the letter “P” in an EBCDIC input stream one Reaction Memoryentry will be used. If the input stream is ASCII or “Tilt and Swivel”code, different entries will be necessary. To convert Reaction Memoryentries from one coding scheme to another, it is only necessary toreorder them. If the coding scheme of an input stream is either unknownor mixed, different sets of detectors can be setup for different codingschemes to determine which is used or to scan for content in mixedstreams. To deal with transcription variations, an individual detectorcan be configured to detect any member of a set of input bytes (anyvowel) for example to overlook the common error of vowel substitution.Another Detection Cell can be set to detect any word boundary character(that is, the byte values corresponding to punctuation spaces, and othercharacters that separate words). A Detection Cell configured to detect acharacter in a word can also be configured to ignore formatting bytessuch as line-feed and carriage return bytes in order to be able torecognize words that start on one line and end on another; and anotherDetection Cell configured to detect word boundary bytes to includeformatting bytes in the list of bytes to which they will respond. Todeal with variable white space a single Detection Cell can be configuredto accept an unspecified number (zero or more) of word boundary bytesfollowed by the first byte of a word.

Shown in FIG. 1 is a typical operational configuration for the GPSTP.The host provides Reference Patterns and access to data sources andaccepts results. The host can be a desktop computer, a server, anetwork, a router. Control and status are communicated between the HostDevice Interface indicated by reference numeral 2 and the GPSTP 1 overline 9. The GPSTP operates in three modes, Load, Diagnostic, and Scan.To prepare for a Scan, a Reference Pattern is loaded into the GPSTP bythe Host Device Interface over line 3. The Reference Pattern, consistingof up to 1 Mb (2²⁰ bits) is transferred to the GPSTP, 32 bits at a timefrom a Reference Pattern image previously compiled and stored in theHost. Diagnostic mode allows GPSTP internal states 4 to be read into theHost. Diagnostic mode can be used to verify that a Reference pattern hasbeen successfully transferred. It can also be used to read out selecteddata from the GPSTP after each input byte as data is scanned in a stepby step fashion to verify correct operation. In Scan mode data arepassed from the data source by the Host Device Interface to the GPSTPone byte at a time over line 5. The GPSTP in two internal clock cyclesdetermines the state of satisfaction of recognition terms up to thecurrent byte and places this result on line 6 to the Host DeviceInterface directly and to the Composite Boolean Logic 7. A resultconsists of 30 bits from the GPSTP that indicate whether each of 30logic thresholds has been reached. The Composite Boolean Logicdetermines whether the input stream up to the current byte has fulfilledthe Reference Pattern and outputs this determination over line 8 to theHost Device Interface. The identity of the current input byte isavailable in the Host Device interface along with the 31 result bits.Host response to and disposition of the result is determined by Hostsoftware depending on how the GPSTP is being used.

As shown in FIG. 2 the processor, indicated by reference numeral 1, ismade up of a 1K×1K bit Pattern Memory 14, two networks: 12 RecognitionNetwork, and 15 Aggregation Network and three supporting components: 11a 32-to-1024 Multiplexer/De-multiplexer, 10 Row Address Decoder, and 13Sequencer. The Host Device Interface may be embodied by a standardoff-the-shelf interface, which may vary depending on the host device andparticular application. The Composite Boolean Logic 7 is a 1 Gb randomaccess memory. The invention assumes that the host device providesaccess to one or more data sources and a random access memory into whichresults can be written. Neither the Host Device Interface nor theComposite Boolean Logic 7 is a part of the present invention.

The Pattern Memory stores a Reference Pattern of 2²⁰ bits (1024×1024)that determine the response of the device to patterns in the inputstream. The Pattern Memory is dual structured. As shown in FIG. 3 thePattern Memory 14 is structured as a conventional random access memory.In the load mode the Row Address Decoder 10 spans all 1024 rows of thePattern Memory, and given a ten bit address via line 16, enables exactlyone row to be read or written. Entire 1K bit rows are written to thePattern Memory from the Multiplexer/De-multiplexer 11 and read from thePattern Memory to the Multiplexer/de-Multiplexer in parallel. Theinvention uses this conventional memory structure to load referencepatterns and to read contents of the Pattern Memory when detailedinternal state data are needed and for diagnostic and testing purposes.The conventional memory structure is not used during scan mode.

Line 3 is the data path between the Host Device Interface 16 and theMultiplexer/de-Multiplexer. It is used to load Reference Patterns intothe Pattern Memory. Line 4 is used to read the contents of the PatternMemory. Line 9 is used for the Host Device Interface and Sequencer 13 tocommunicate commands and status. Line 20 is used by the Sequencer tocontrol the Multiplexer/de-Multiplexer. Line 17 is a row select/enableline (one of 1024) used to select the row specified by the binary valueof the 10 bits supplied by line 16. Line 16 is used by the Sequencer todirect Reference Pattern data to the correct row and to specify whichrow is to be read in a Read from Pattern memory operation. Line 19 isused by the Sequencer to command the Pattern Memory (in load anddiagnostic modes) to write to and read. Line 18 is used by the Sequencerto set the mode (Load, Diagnostic, Scan) of the Row Address Decoder 10.

The second Pattern Memory structure, the Scan mode structure, is shownin FIG. 4. In Scan mode the Pattern Memory is treated as three 256×1024bit partitions one 96×1024 bit partition and a 160×1024 bit partition.The detailed Scan mode Pattern Memory structure is shown in Table 1. Onecolumn of the Pattern Memory belongs to each Detection Cell. The columnPM_(i) holds all the bits that define the behavior of DC_(i) and itsrelation to all the other Detection Cells.

TABLE 1 Scan Mode Pattern Memory Structure PM_(row) SymbolDescription/Name Recognition Network 0 S Cell's Current State  96 Rows 1IS Initial State 32-86 (1-of-6) SFP Select Forward Propagation 33-87(1-of-6) SRP Select Reverse Propagation 34-88 (1-of-6) FPS ForwardPropagation Switch 35-89 (1-of-6) RPS Reverse Propagation Switch 36-90(1-of-6) AFP Accept Forward Propagation 37-91 (1-of-6) ARP AcceptReverse Propagation 92-95 RFU Reserved for Future Use Aggregation 96-220(1-of-4) RLB Results Latch Bits 160 Rows Net 97-221(1-of-4) RIB Reservedfor Future Use 98-222 (1-of-4) LNL Latch No Latch 99-223 (1-of-4) SLGSelect Logic Group Bits 160-192(1-of-4) RBM Reserved for Future Use 255TLIV Threshold Logic initial values Reaction 256-511 MA Auto-EnableMemory 768 Rows Memory 512-767 MS Successor-Enable Memory 768-1023 MRResponse Memory

In Scan mode the three 256×1024 bit partitions are treated as threeseparate conventional memories accessed simultaneously with the sameaddress, the binary value of the input byte from the scan stream. Oneach input byte cycle one 1024 bit row is read from each of the threememories which are accessed independently using separate sections of theRow Address Decoder so that 3×1024 bits are accessed on the same clockcycle. The remaining 96×1024 bits in the Recognition Network and the160×1024 bits in the Aggregation Network are not used conventionally inScan mode. The circuits that use these memory partitions access thememory bits directly, not using the bit lines by which they are accessedin the load and diagnostic modes.

As shown in FIG. 5 the Recognition Network comprises an orderedplurality of two-state deterministic finite state automata (DFSA) calledDetection Cells indicated by reference numeral 29, a Reaction Memory 25,and a State-Change Routing Matrix (SCM) 32. In Scan mode as each inputbyte b is presented to the GPSTP the sequencer sets Clock C 33 to “1”. Cis applied simultaneously to the Reaction Memory 25, to all DetectionCells 29, and to the State-Change Routing Matrix 32. This uses 1024 bitsMR_(b,0-1023) 27, MA_(b,0-1023) 26, MS_(b,0-1023) 28 each from theResponse Memory, Auto-Enable, and Successor-Enable memories. This causeseach Detection Cell DC_(i) 29 to pass the values of S_(i)^ MR_(b,i) 24to the Aggregation Network and S_(i) ^ MS_(b,i) 30 to the SCM 32. TheSCM returns ∪E_(i) 31 (the disjunctive sum of Successor-Enable outputsof DC_(i)'s Precursors). The foregoing operations are accomplished inone clock cycle.

Detection Logic Method

To determine whether a byte in a stream satisfies a qualifyingcondition, this invention does not compare the byte to a stored bytevalue. Instead, the invention employs a method of representingqualifying conditions called counter representation. To understandcounter representation consider the following:

Suppose it is desired to detect the occurrence of an uppercase letter“L”. Counter representation represents the “L” using the binary value ofthe digital symbol 01001100_(B)=76_(D) to write a 1 into a responsememory (256×1 bits). FIG. 6 a depicts a segment of a 256×1 bit memory.The decimal numerical column is the row number or row address of eachbit. The bit value is given in the center column, and the right columnis the ASCII equivalent of the binary value of the row address. And thearrow indicates the row into which a 1 is written for the counterrepresentation of “L”. To detect an “L” in a stream of bytes, it is onlynecessary to “read” the contents of the response memory using the binaryvalue of each byte as an address FIG. 6 b: the output of the responsememory will be 0 for any value other than 76 _(D) and 1 only if thevalue is 76 _(D). Note that counter representation is independent ofcoding scheme; as long as the counter representation bit is writtenusing the same coding scheme as that to be scanned. All that isnecessary to change from one coding scheme to another is re-order theresponse memory rows according to the second scheme.

Term Logic Method

To detect patterns longer than a single byte a number of said DetectionCells can be strung together in a linear arrangement with the output ofeach Detection Cell connected to its immediate successor. TheseDetection Cells, in addition to a response memory are provided with anadditional bit of memory to indicate the state (“OFF” or “ON”). In FIG.4 the response memories of four Detection Cells, DC₁-DC₄, have been setup to detect the string “Love”. Output of Detection Cell DC₁ isconnected to the state memory (or simply state) of DC₂, the output ofDC₂ to the state of DC₃, etc. The state S₁ of DC₁ is initialized to “ON”and all of the initial states of the other Detection Cells to “OFF”.

Presenting the string “Hope and Love” to all four Detection Cells (eachbyte presented to all Detection Cells simultaneously), none of theDetection Cells would respond (output a 1) until the “L” is presented,at which point the output of DC₁ enables DC₂ (sets its state to “ON” bywriting a 1 into its state memory). Similarly, as “o”, “v”, and “e” arepresented the detection is propagated from one Detection Cell to thenext, until detection of the whole string is indicated by the output ofDC₁. (The preferred embodiment type of memory used for Detection Cellstates allows the current contents of the memory to be read on the sameclock signal as that used to write a new value, so detection propagationdoes not propagate prematurely.) Since it enables the Detection Cell'ssuccessor when it's value is one, this 256×1 memory is called theSuccessor-Enable Memory (MS_(i)) and its output for an input byte b isdenoted MS_(b,i). The state bit is used to control output from theDetection Cell. The Successor-Enable response of DC_(i) for an inputbyte b is given by the expression (the “^” denotes the logical ANDoperator):S_(i) ^MS_(b,i)

Note that the output of each Detection Cell is written into itssuccessor's state memory even if it is zero. This has the effect ofplacing the successor Detection Cell in the “OFF” state for the nextinput byte. (Again, because of the built in delay in this type of memoryif a Detection Cell is “ON” when a byte is presented it will output thevalue at the location addressed by that byte. The state value relayedfrom its predecessor sets its state for the next input byte.) DetectionCells being disabled (state set to “OFF”) between each bytes is adesirable, other wise, “Lark and dove” would be erroneously detected:the “L” in “Lark” enabling DC₂ that then picks up the detectionpropagation with the “o” in “Dove”.

But if DC₁ is disabled between input bytes, the initial “L” will only befound if the first input byte is an “L”. Initial Detection Cells couldbe treated differently from the others, but for reasons that will beapparent below treating all Detection Cells the same is highly desirableas special treatment for initial Detection Cells would diminish thegenerality of the device. So, a general mechanism for keeping an initialDetection Cell enabled is needed, a mechanism that can also be used tomeet other requirements.

Such a mechanism can be derived from counter representation as well. Ifall Detection Cells are provided with an 256×2 response memory insteadof 256×1, the additional bits can be used by an initial Detection Cellto keep itself enabled; thus, this 256×1, memory is called the DetectionCell's Auto-Enable Memory and is denoted MA_(i). Like theSuccessor-enable Memory, its output is controlled by the DetectionCell's state S_(i), and its Auto-Enable output is given by theexpressionS_(i) ^MA_(b,i)

A Detection Cell's Auto-Enable Memory can also be used selectively tomeet other needs (such as precision wild cards and ignoring missing orextra bytes, etc., as will be addressed later.) To keep an initialDetection Cell in the “ON” state all 256 of its Auto-Enable bits are setto 1. Each input byte addresses both the Successor-Enable response andthe Auto-Enable response at the same time. A Detection Cell'sauto-enable bit together with its predecessor's successor-enable bitenable the Detection Cell if either is a 1. The next state of D_(i) isgiven by the expression (the “v” denotes the logical OR function):S_(i) ^MA_(b,i) v S_(i) ^MS_(b,i−1)

In the simple cases discussed above it is sufficient to treat theSuccessor-Enable output of the final Detection Cell as a reaction to theconditions expressed in all of the Detection Cells. But the objective ofthe present invention requires that it be able to scan the contents of adata stream for the presence of combinations of many pattern elements atonce. A pattern will involve many times the number of Detection Cellsdiscussed so far, in some cases many hundreds or thousands of DetectionCells. That is to say any final Detection Cell may be followedimmediately by the initial Detection Cell for the next set. Thus, amechanism is needed for the reaction that is separate from theSuccessor-Enable response and Auto-Enable memories. The response memoryDetection Cell is extended to 256×3 bits. The third bit is called theReaction bit and the third memory column, the Reaction Memory. Thecolumn is denoted MR_(i) and the Reaction bit corresponding to byte b isdenoted MR_(b,i). MR_(b,i)=1 for Detection Cell if the byte b can be afinal byte value for a string of Detection Cells. As with theSuccessor-Enable response and Auto-Enable memories the Detection Cell'sState S_(i) controls the Detection Cell's Reaction output that isdenoted:S_(i) ^MR_(b,i)

A data source input character ‘b’ may be an initial value for onestring, a final value for another string, a sustaining value for yetanother and an irrelevant character to another.

Some definitions before proceeding: A Detection Cell D_(i) comprises aone bit State (Memory) S_(i); a 256×3 bit Response Memory T_(i),comprising a 256×1 Successor-Enable Memory MA_(i), a 256×1 Auto-EnableMemory MA_(i), and a 256×1 Reaction Memory MR_(i); and circuits thatyield outputs:S_(i) ^MA_(b,i) , S _(i) ^MA_(b,i), and S_(i) ^MR_(b,i,)and next State:S_(i) ^MR_(b,i.)

The three bits MR_(b,i), MS_(b,i), MA_(b,i,)=T_(i) is called a ReactionTriple . A Detection Cell DC_(i) is said to be “programmed” or“configured” when its Response Memory is loaded and it's State S_(i) isinitialized. While the bit pattern that determines the Detection Cell'sbehavior (called a Feature) is distinct from the Cell itself, they areso intimately related that the terms will be used interchangeably inthis document, except where the distinction is germane. A Simple Term isa contiguous set of Features the first of which is initialized to the“ON” state, will to remain “ON” for any input byte, and will enable itsimmediate successor in response to at least one input byte value; thelast of which is initialized to the “OFF state, iwill remain “Off”unless enabled by its immediate predecessor, will acknowledgerecognition of the Term when it is in the “ON” state in response to aterm ending input byte value by sending a 1 to the Aggregation Network;and whose interior features are initialized to the “Off” state, when“ON” will detect at least one input byte value and in response enabletheir immediate successors, and will return to the “OFF” state unlessenabled by their immediate predecessors. In less formal terms, a Term'sfirst Feature is always alert for an input byte that begins a string ofinterest. It responds to such a byte by enabling its neighbor (immediatesuccessor) for the next input byte cycle. (Now both the first and secondFeatures are enabled.) While the second feature is “ON” it responds toan input byte in its list of satisfying bytes by enabling its neighborfor the next byte input cycle (and “going back to sleep”). This “wave”of detection is thus passed from Feature to neighbor until the Term'slast Feature is enabled. If a satisfying byte is the next input, aninput string satisfying the Term has been recognized and the Term's lastfeature sends a 1 to the Aggregation Network.

The Feature is the fundamental unit of detection and the Term is thefundamental unit of meaning. A Feature is said to detect a byte; adetected byte is said to satisfy the feature's detection criteria or totrigger its response. A string is recognized by a Term, a recognizedstring is said to satisfy the Term's qualifying criteria, or simply tosatisfy the term.

A formulaic description of an individual Detection Cell is shown in FIG.8. The state S_(i) of the Detection Cell is held in a delay flip-flopmemory internal to the cell. The formulae method by which the DetectionCell operationalizes it's “ON”-“OFF” behavior. Each input symbol (byte)is used to address the Reaction Memory for Reaction Triples for allDetection Cells. For DC_(i), indicated by reference numeral 29 thetriple consists of MR_(b,i), 27;. MA_(b,i), 26; and MS_(b,i), 28. IfDC_(i) is “ON”, Si=1 and so:24 S_(i) ^MR_(b,i)=MR_(b,i)S_(i) ^MA_(b,i)=MA_(b,i)30 S_(i) ^MS_(b,i)=MS_(b,i).(S_(i) ^ MA_(b,i) is used internally and is not shown here, but will beshown in FIG. 18 as a part of the apparatus.)

If MR_(b,i)=1 then a Term has been recognized and the 1 is acted on bythe Aggregation Network; if MA_(b,i)=1, then the Detection Cell isenabled for the next input byte cycle; if MS_(b,i)=1, the DetectionCell's successors (neighbor and other Detection Cells that may beenabled by DC_(i)) are enabled for the next byte input cycle. If thecell is “Off” S_(i)=0 and:S_(i) ^MR_(b,i)=0S_(i) ^MA_(b,i)=0S_(i) ^MS_(b,i)=0.A term has not been recognized and unless otherwise enabled theDetection Cell and its successors are set to the “OFF” state for thenext byte input cycle. (This is exactly the same effect as an enabledDetection Cell for which the Reaction Triple RMb,i=(0,0,0)). This isdone on a single clock cycle C 33 for all Detection Cellssimultaneously, thus engaging 3×1024 bits the Reaction Memory and 1024bits from the states of the Detection cells in the primary response toeach input byte. Additional multiples of 1024 bits are engaged insecondary responses (secondary in functionality, but not in clock cycletime).Complex Term Method

In order to recognize strings differently expressing equivalent meaninga combination of large capacity (large numbers of Detection Cells) andmore complex Terms are required. Strings with the same meaning as “dogchases cat” may be expressed as “poodle pursues puma”, for example. Andsubstitutions can be made for any of the three words in the string andstill produce a string that will be of interest to a person with respectto some body of text. Depending on the robustness of the user ontologythere may be dozens of substitutions for each word. If each noun isvaried through 12 alternatives and the verb is varied through 5, thenumber of different strings that might be of interest would be 770. Ifstring variants' average length is 20, more than 14,000 Detection Cellswould be required using Simple Terms. The Complex Term method used bythe invention, however, reduces that number dramatically to fewer than600.

The meaning of three words need to be clarified before proceeding; theydefine elements that correspond to the beginning, middle, and end of theSimple Term.

A Term Prefix is a set of contiguous Features such that for the firstDC_(f):

-   -   MA_(b,f)=1 for 0≦b≦255    -   MS_(b,f)=1 for at least one b    -   MR_(b,f)=0 for 0≦b≦255

And for subsequent Features DC_(i):

-   -   MA_(b,i)=0 for 0≦b≦255    -   MS_(b,i)=1 for at least one b    -   MR_(b,i)=0 for 0≦b≦255.

A Term Infix is a set of contiguous Features DC_(f) such that:

-   -   MA_(b,i)=0 for 0≦b≦255    -   MS_(b,i)=1 for at least one b    -   MR_(b,i)=0 for 0≦b≦255.        A Term Suffix is a set of contiguous Features such that for the        first DC_(f):    -   MA_(b,i)=0 for 0≦b≦255    -   MS_(b,i)=1 for at least one b    -   MR_(b,i)=0 for 0≦b≦255

And for that for the last DC_(L):

-   -   MA_(b,L)=0 for 0≦b≦255    -   MS_(b,L)=0 for 0≦b≦255    -   MR_(b,L)=1 for at least one b.

Accordingly, a Term can be re-defined as one or more Term PrefixesFollowed by One or more Term Infixes followed by one or more TermSuffixes. Simple Term omits the “or more”. A Complex Term includes atleast one of the three. A Complex Term, like the “dog chases cat”example can have more than one beginning, middle, and end.

The method the invention uses to realize the Complex Term is theState-Change Routing Matrix. The State-Change Routing Matrix FIG. 9 isan ordered 2 dimensional plurality of State-Change Routing Cells(SRC's), indicated by reference numeral 32, that form a reticulum ofpotential interconnections between Precursor Detection Cells andSuccessor Detection Cells. Any Detection Cell can be a Precursor,Successor, or both. Most commonly, each Detection Cell in a Simple Term(except the last) is a Precursor of its nearest “right” neighbor and(except the first) is a Successor of its nearest “left” neighbor. TheReference Pattern determines actual interconnections from specificPrecursors to specific Successors.

The S_(i) ^ MS_(b,i) 30 output of each Detection Cell DC_(i) (aspotential Precursor) is connected to each of 10 SRC's (SRC1 _(,i)-SRC1_(0,i)) 32, this means that any enabled Detection Cell that is triggeredby an input b will output a 1 to all 10 of these SRC's 32. Similarly,each column of 10 SRC's (SRC1 _(,i)-SRC1 _(0,i)) 32 is connected bylines 31 to a Detection Cell DC_(i) (as potential Successor). Theselines carry ∪SE_(i), the disjunctive sum of successor enable (S_(pi) ^MS_(b,pi)) 30 outputs of DC_(i)'s Precursors. ∪SE_(i)=1 whenever any ofthe DC_(i)'s Precursors (S_(pi) ^ MS_(b,pi))=1. In DC_(i), S_(i)=∪SE_(i)v (S_(i) ^ MA_(b,i)) becomes DC_(i)'s new state. This state change (forall Detection Cells) occurs on the “trailing edge” of the same clock C33 as is generation of output to the Aggregation Network (FIGS. 4, 15).

The State-Change Routing Cell 32 provides the method of connecting TermPrefixes to Term Infixes and Term Suffixes as well as from Term Infixesto Term Suffixes, thereby providing Enabling Propagation Paths fromPrecursor to Successor. It is a hub controlling Successor Enable signalsfrom the Detection Cell as Precursor via Enabling Propagation Paths toDetection Cells as Successors.

The formulaic description of an individual State-Change Routing Cellindicated by reference numeral 39 is shown in FIG. 10. The SuccessorEnable input SE_(i) (S_(i) ^ MS_(b,i)) enters SRC_(i) via line 32. IfDC_(i) is to be a Precursor, either the Select Forward Propagation (SFP)switch or the Select Reverse Propagation (SRP) switch or both are set to“ON”=1. These switches allow the Successor Enable to be passed fromSRC_(n,i) to SRC_(n,i−1) and SRC_(n,i+1) (in possible combination withsignals from Forward Enabling Propagation Paths and Reverse EnablingPropagation Paths on lines FP_(j,i−1) 37 and RP_(j,i+1) 41. The ForwardPropagation Switch (FPS_(j,i)) is “On” if a Forward Enabling PropagationPath from SRC_(n,i−1) is to be passed to SRC_(n,i+1), and “Off” if thatpath is to end at DC_(i). The Reverse Propagation Switch (RPS_(j,i)) issimilarly set to pass on or terminate a Reverse Enabling PropagationPath from SRC_(n,i+1). The disjunctive combination of Successor Enablesignals originating from DC_(i) and those arriving at SRCn,i are passedto SRC_(n,i−1) and SRC_(n,i+1) via lines (FP_(j,i−1)^FPS_(j,i)) v SE_(i)^ SFP_(j,i)) 40 and (RP_(j,i+1) ^ RPS_(j,i))v(SRP_(j,i) ^ SE_(i)) 36. IfDC_(i) is to be a Successor either or both of the Accept ForwardPropagation and Accept Reverse Propagation are “ON”. ∪SE_(j+1,i), theSuccessor Enable signal accumulated from SRC1 _(0 to j+1,i) is receivedvia line 38. The disjunctive sum of ∪SE_(j+1,i), and those accepted fromeither propagation direction ∪SE_(j+1,i) v (FPj_(,i−1) ^AFP_(j,i))v(RP_(j,i+1) ^ARP_(j,i)) 42 is passed to SRC_(j−1,i) andthence from SRC1 _(,i to) DC_(i), in the form of ∪SE_(i) where it iscombined ∪SE_(i) v (S_(i) ^ MA_(b,i)) to determine the new state ofDC_(i).

FIG. 11 is a block diagram illustrating use of the State-Change RoutingMatrix to provide neighbor-enabling paths, indicated by bold arrows, fora Simple Term. DC₀ through DC₂ each has at least one Successor EnableBit set to 1, connecting Successor Enable from one Detector Cell to thenext SRC via lines indicated by reference numerals 30, and 45. SRC_(1,0)to SRC_(1,3) are set to Select Forward Propagation lines 29, andSRC_(1,3) has at least one Response bit set to 1 as indicated by line23. These settings provide a Forward Enable Propagation Path from DC₀ toDC₁, DC₁ to DC₂, and from DC₂ to DC₃. Pass-through of Successor Enable(Forward Propagation Switch) is “OFF” in DC₀ through DC₃, so SuccessorEnable propagation is forced through the Detection Cells one at a time.

FIG. 12 with greatly simplified graphics shows two examples of use ofthe State-Change Routing Matrix. First features are indicated by heavybox borders. The drawing in FIG. 12A illustrates a single Complex Termwith a single Term Prefix, three alternate Term Infixes, and a singleTerm Suffix. It is intended to detect any of four words that sharemeanings with the word “cat”; only whole words will do (“caterpillars”and “catalepsy” will not satisfy). The Term's first feature has all ofits Auto-Enable bits set to 1 to keep it perpetually “ON”. And all ofits Successor-Enable bits whose row addresses correspond to aninter-word character (spaces, tabs, etc.) are set to 1. So every time aninter-word character is input, this first Detection Cell is triggeredand outputs a 1 to the State-Change Routing Matrix. Using EnablingPropagation Paths in State-Change Routing Matrix rows 2 and 3 (Row 1,used in this case for neighbor enable only, is not shown to avoidclutter. Though an enable path to neighbors is not shown, it can beassumed) the features beginning the strings “cats”, “felines”,“siamese”, and “tabby” are enabled for the next byte input cycle. Thisis an illustration of “fan out”. For the next input byte five DetectionCells are “ON”. If the next byte is a “c”, an “f”, an “s” or a “t”, thedetection is propagated to the Detection Cell following the triggeredDetection Cell enabling it for the next byte input cycle. For this byteinput cycle, two Detection Cells are “ON”. Assuming that at least one ofthe “cat” words is encountered, the last Detection Cell in thecorresponding Feature string will at some time be enabled and satisfied.Enabling Propagation Paths, indicated by the dashed line connecting thelast Detection Cells of each of the four Feature strings to the featurewith the capital sigma. This is an example of “fan in”. For the “sigma”Detection Cell Response Bits whose row addresses correspond to thebinary value of bytes that represent inter-word or punctuationcharacters are set to 1. If the next input byte is one of these, a 1 isoutput from this Detection Cell to The Aggregation Network.

FIG. 12B. Is intended to recognize members of the set of strings thatbegin with either the string “cat” or “lion” (not necessarily wholewords) followed by the word “chase” followed by either of the words“dog” or “fido”. It comprises two Term Prefixes, a single Term Infix,and two Term Suffixes. The Enabling Propagation Path from the “t”feature in the “cat” feature string to the “lower case sigma” Featurebetween the “lion” and “chase” feature strings allows both Term Prefixesto operate in parallel (There is no Enabling Propagation Path betweenthe “t” Feature in the “cat” feature string and the “I” first Feature ofthe “lion” Feature string because having no Precursor is part of thedefinition of “first” Feature in a Term or Term Prefix.) The “lowercasesigma” Features have both their Auto-Enable and Successor-Enable bitsset to 1 for row addresses corresponding to the binary values of bytesrepresenting inter-word characters. This arrangement allows them todetect variable numbers of characters between words still propagate thedetection “wave” to the first word character Feature in the followingFeature String. Once enabled, the Auto-Enable bit settings of theseDetection Cells keep them “ON” as long as they continue to receiveinter-word characters and their Successor-Enable bit settings keep thenext Detection Cell enabled for the next input byte cycle. So when thefirst word-character following an inter-word string is received, the“lowercase sigma” feature is not satisfied, but if that word-characteris a “c”, the “c” Feature of the “chase” Feature string is satisfied andcontinues the detection “wave”. When the second “lowercase sigma”Feature is triggered both the “d” feature In the “dog” Feature stringand the “f” Feature in the “fido” are enabled, the former by theimplicit Enabling Propagation Path between Detection Cells and their“right” neighbors (when the neighbor is not the first Feature in a TermPrefix) and the later via the Enabling Propagation Path from the second“lowercase sigma” Feature. The terminal Feature in each of these Featurestrings is a “capital sigma” Feature that has Response Bits whose rowaddresses correspond to the binary values of bytes representinginter-word or punctuation characters to set to 1 and all other bits inits Reaction Memory set to 0.

It is stressed here that phrases like “implicit Enabling PropagationPath between Detection Cells and their ‘right’ neighbors” refer to thisexplication and not to the device itself. The apparatus is completelygeneral; its behavior is determined by the contents of its patternmemory. Responses of Detection Cells are determined by the contents oftheir Reaction Memories; their interaction with other Detection Cells isdetermined by the values of delay flip-flop memories that controlswitches in the State-Change Propagation Matrix (and these switchmemories like all cells in the invention are part of the Pattern Memory.

One of the important benefits of the State-Change Propagation Matrixarchitecture is that it provides a method for tolerating manufacturingerrors. The number of flaws in a semiconductor is proportional itsnumber of gates. The more gates, the smaller the feature size, thegreater the number of flaws. Flaws are a serious cost driver either inthe form of reduced yield or remediation cost. But the columnarorganization of the pattern memory of the invention allows chips to beused even with multiple, possibly many flaws. All of the memory (1024bits) associated with a Detection Cell is a column of the Pattern Memoryand all of the “additional” gates (those not used for reading andwriting whole memory rows) associated with a Detection Cell areco-located with the Detection Cell's memory column. Each Detection Celland its memory column is a relatively independent module. If it containsflaws and there is a means to bridge between its “left” and “right”neighbors it can be ignored. FIG. 13 illustrates the means by which theState-Change Propagation Matrix can be used to “bridge” over a badDetection Cell by configuring an Enabling Propagation Path from the“left” neighbor of the bad Detection to its “right” neighbor. Theknowledge that a Detection Cell or any of its related memory cells isbad can be established by ordinary diagnostic routines used to testsemiconductor chips and provided to the adjunct software that translatesuser information desires into Reference Patterns. That softwareroutinely constructs Enabling Propagation Paths for recognizing complexstring sets. It can also construct them for the purpose of “bridging”over bad Detection Cells or flaws in any part of the Pattern Memoryassociated with a Detection Cell. The only cases not susceptible to thismeans of tolerating manufacturing flaws are flaws in theMultiplexer/De-Multiplexer, flaws in multiple State-Change Routing Cellsassociated with a single Detection Cell, and flaws in the part of thePattern Memory associated with the Aggregation network.Multiplexer/De-Multiplexer flaws can be addressed independently byredundancy, multiple SRC flaws to the degree that this method would becompromised are likely to be rare enough not to have a significantaffect on yield, and Aggregation Network flaws can be addressed in theAggregation Network itself.

Aggregation Method

The Aggregation Network organizes term recognition results into acoherent whole. As shown in FIG. 14 it comprises an Aggregation RoutingMatrix indicated by reference numeral 43, and a Threshold Logic 45. Onclock C 33 each the selected response bits S₀ ^ MR_(b,0)-S₁₀₂₃ ^MR_(b,1023) 24 from the Recognition Network is routed via one or moreGroup Logic Lines GLL₁-GLL₃₀ 44 to the Threshold Logic 45.

Aggregation Routing Matrix

The Aggregation Routing Matrix as shown in FIG. 15, comprises an orderedtwo-dimensional plurality of Aggregation Routing Cells (ARC) asindicated by reference numeral 46. There is one ARC for each S_(i) ^MR_(b,i)-GLL_(j) pair, denoted ARC_(j,i) 46. The subscripts “j” and “i”also denote the row and column where ARC_(j,i) 46 is located. The outputGLL_(j,i), 47 from each ARC_(j,i) is denoted with the same subscript. Onclock C 33 each S_(i) ^ MR_(b,i) 24 is input independently to each ARCwith the same column subscript and the output of each as shown in FIG.16 is given by:GLL _(j,i) =GLL _(j,i−1) v[(Sm ^ MR _(b,i))^(˜RLB _(j,i) v LNL_(j,i))^SLG _(j,i))]where RLB is the Result Latch Bit, LNL is the Latch-No-Latch bit, andSLG is the Select Logic Group bit. In Load mode SLG is initialized to 1to direct output from DCi to GLLj, otherwise it is set to 0. The valueof SLG does not change in Scan Mode. RLB is set to the complement of SLGin Load Mode. It can also be initialized to the value of SLG by the Hostvia the INI command. LNL is set to 0 in Load mode if the ARC is to actas a latch and 1 if it is not. The value of LNL does not change in Scanmode. If SLG_(j,i)=0, then the connection is not “selected” andGLL_(ji)=0 without regard to outputs from DC_(i) or RLB and LNLsettings. If SLG_(j,i)=1, then RLB is initialized to Zero. With theseinitial settings, the first time Sm ^ MR_(b,i)=1, GLL_(ji)=1 and 44GLLj=∪GLLji (0≦i≦1023)=1; and RLB_(j,i) changes from 0 to 1; this hasthe effecting of “latching” ARC_(j,i) to 0 for future input cycles(until RLB_(j,i) is reset to 0). LNL=0 is used for those cases in whichit is desirable to acknowledge the occurrence of a string satisfying aTerm only once in a given context rather than acknowledging it on everytime the Term is satisfied. These are cases where it is of interest toknow that n different Terms are satisfied as opposed to knowing that aTerm is satisfied n times. LNL provides for the latter, however. If LNLis set to 1, every occurrence of a string satisfying the Term whose lastFeature is DC_(i) will place a 1 on line GLL_(j,i). As indicated on FIG.15 by reference numeral 44 GLL_(j)=∪GLL_(j,i) (0≦i≦1023). That is,whenever any of the GLL_(j,i)=1 then GLL_(j)=1. Informally, a GLL has avalue of 1 whenever any of the Terms for which it is selected issatisfied.Threshold Logic Method

As shown in FIG. 17 the Threshold Logic comprises an ordered pluralityof identical Group Threshold Logic cells (GTLs) indicated by referencenumeral 49. In Load mode, a threshold value is written to each GTL 49.In Scan mode each time a GTL 49 receives 1 from its GLL 44, thethreshold value in decremented by one. While the threshold value isgreater than zero the GTL's output 50 TL is zero. When the thresholdvalue is decremented to zero, GTL's output 50 TL is 1 and remains 1until the GTL's 49 threshold is reset to the initial threshold value.(There is a 10 bit memory for the initial threshold value in each GTL 49so it can be reset—via the INI 34 line—without reloading it from theHost).

Interpretation of the threshold differs depending on the value of theLNL. If LNL=0 then the threshold has logic meaning, of LNL=1, themeaning is numeric. If, for example the results of ten Terms are routedto the same GTL, and its threshold is 6, the meaning of its output isthe truth value of “Six of these ten terms are present.” If TL=0, “Sixof these ten terms are present.” is false. If TL=1, “Six of these tenterms are present.” is true. If the threshold is 1, the meaning of theTL value is equivalent to a disjunctive sum over the scan outcomes ofthe ten Terms, TL=1 means “At least one of the ten Terms is satisfied.”If the threshold is 10, it has the meaning of the conjunctive sum overthe scan outcomes of the ten terms, that is, “All of the Terms aresatisfied.”

If an individual Term routed to a GTL has its LNL=1 and the GTL has athreshold of n, the meaning of the TL is the truth value of “This Termhas been satisfied at least n times”.

Boolean Logic

Up to thirty Threshold Logic binary digits are output to the Host (viathe Host Device Interface). For many applications it may be adequate tohave the Host dispose of the 30 bit result unaided. However, it may alsobe useful, especially with regard to performance to reduce the 30 bitresult to a single yes/no one bit result, to determine whether thesubject digital object “is in the set of items of interest” or not. Thiscan be done with the use of a 1 GB random access memory. In preparingthe Reference Pattern, the Host also processes a Boolean logicexpression provided by the user and the user's software. If n GTL's areused the expression will have n logic variables. By assigning each ofthe logic variables to a binary digit in an digit binary variable and byincrementing the binary variable fro 0 to 2^(n)−1, all combinations oftruth values of the logic variables can be generated. Evaluatingexpression for every combination produces all of the possible truthvaluations of the expression. Using the numerical value of the n-bitbinary variable for each combination as an address the 1-bit truth valuefor that combination can be stored in the random access memory. Then inScan mode the output of the Threshold Logic is presented to the addresslines of the random access memory to read the pre-calculated truth valuefor that combination of TL values.

Apparatus

The foregoing discussion has been primarily directed at the method ofthe invention. The following describes more details of the apparatusthat are pertinent to achieving the high throughput and low cost.

Detection Cell Detail

Details of the Detection Cell are shown in FIG. 18. S_(i), indicated byreference numeral 54 is a delay type flip-flop memory whose currentvalue is the state of the Detection Cell. Clock C Enables the threeReaction Memories to read a 1024 bit word from each from the row addressdetermined by the current byte b from the input stream. This readpresents values for MR_(b,i) 27, MS_(b,i) 28, and MA_(b,i) 26, to theDC_(i). The same C 33, on its leading edge, enables S_(i) ^ MR_(b,i) 27to be output to the Aggregation Network and S_(i) ^ MS_(b,i) 30 to beoutput to the State-Change Routing Matrix, that systolicly returns∪E_(i) 31, the Disjunctive sum of DC_(i)'s Precursors' Successor-Enableoutputs. On its trailing edge, C 33 enables ∪E_(i) v (S_(i) ^ MA_(b,i))54 to be written into S_(i), updating DC_(i)'s state.

State-Change Routing Cell Detail

As shown in FIG. 19 the State-Change Routing Cell comprises sixswitches, each consisting of a delay type flip-flop memory and an “AND”gate. The Select Forward Propagation bit (SFP_(j,i) indicated byreference numeral 60) and its “And” gate control entry of DC_(i)'sSuccessor-Enable signal E_(i)=S_(i) ^ MS_(b,i) 32 to the ForwardPropagation line FP_(j,i). The Select Reverse Propagation bit (SRP_(j,i)59) and its “And” gate control entry of DC_(i)'s Successor-Enable signalE_(i) 32 to the Reverse Propagation line RP_(j,i). The ReversePropagation Switch (RPS_(j,i) 58) and its “And” gate control passage ofthe Precursor Successors-Enable signals from Reverse Propagation lineRP_(j,i+1) 41 to Reverse Propagation line RP_(j,i) 36. The ForwardPropagation Switch (FPS_(j,i) 57) and its “And” gate control passage ofthe signals from Forward Propagation line FP_(j,i−1) 37 to ForwardPropagation line FP_(j,i) 40. The Accept Forward Propagation bit(AFP_(j,i) 56) and its “And” gate control acceptance of the signal fromForward Propagation line FP_(j,i−1) 37 to the disjunctive sum ofSuccessor-Enable signals line ∪E_(j,i) 42. The Accept ReversePropagation bit (ARP_(j,i) 55) and its “And” gate control acceptance ofthe signal from Reverse Propagation line RP_(j,i+1) 41 to thedisjunctive sum of Successor-Enable signals line ∪E_(j,i) 42.

The values of all six of these memory cells are written in the Load modeand do not change in the Scan Mode. The State-Change Routing Matrix isstatic, though the traffic over it is dynamic.

Aggregation Routing Cell Detail

The Aggregation Routing Cell, ARC_(j,i), shown in FIG. 20 is responsiblefor routing the output of Detection Cell DC_(i), S_(i) ^ MR_(b,i),indicated by reference numeral 24, to Group threshold Logic GTL_(j) viaGroup Logic Line GLL_(j) 48. The subscripts “j” and “i” also denote therow and column location of the cell in the Aggregation Routing Matrix.The output GLL_(j,i), 48 from ARC_(j,i) is denoted with the samesubscript. On clock C 35 each S_(i) ^ MR_(b,i) is input independently toeach ARC with the same column subscript. The output GLL_(j,i) 48 isgiven by:GLL _(j,i) =GLL _(j,i−1) v [(S _(i) ^MR _(b,i))^(˜RLB _(j,i) v LNL_(j,i))^SLG _(j,i))]where RLB is the Result Latch Bit, LNL is the Latch-No-Latch bit, andSLG is the Select Logic Group bit. In Load mode SLG is initialized to 1to direct output from DC_(i) to GTL_(j), otherwise it is set to 0. Thevalue of SLG does not change in Scan Mode. RLB is set to the complementof SLG in Load mode. It can also be initialized to the value of SLG bythe Host via the INI command (between contexts—documents, images,routing packets, etc,). LNL is set to 0 in Load mode if the ARC is toact as a latch and 1 if it is not. The value of LNL does not change inScan mode. If SLG_(j,i)=0, then the connection is not “selected” andGLL_(ji)=0 without regard to outputs from DC_(i) or RLB and LNLsettings. If SLG_(j,i)=1, then RLB is initialized to Zero. With theseinitial settings, the first time S_(i) ^ MR_(b,i)=1, GLL_(ji)=1. AndRLB_(j,i) changes from 0 to 1; this has the effect of “latching”ARC_(j,i) to 0 for future input cycles (until RLB_(j,i) is reset to 0).LNL=0 is used for those cases in which it is desirable to acknowledgethe occurrence of a string satisfying a Term only once in a givencontext rather than acknowledging it on every time the Term issatisfied. These are cases where it is of interest to know that ndifferent Terms are satisfied as opposed to knowing that a Term issatisfied n times. LNL=1 provides for the latter, however. If LNL is setto 1, every occurrence of a string satisfying the Term whose lastFeature is DC_(i) will place a 1 on line GLL_(j,i).Group Threshold Logic Cell Detail

The Group Threshold Logic GTL_(j) shown in FIG. 21 is one of an orderedplurality of identical Group Threshold Logic cells. In Load mode, athreshold value is written to each GTL. In Scan mode each time GTL_(j)receives 1 from GLL_(j) 44, its value in decremented by one. While itsvalue is greater than zero the GTL_(j)'s output 50 TL_(j) is zero. Whenthe value is decremented to zero, GTL_(j)'s output 50 TL is 1 andremains 1 (until it is reset to the initial threshold value. (There is a10 bit memory for the initial threshold value in each GTL so it can bereset—via the INI 34 line—without reloading it from the Host).

Interpretation of GTL_(j)'s threshold differs depending on the value ofthe LNL setting of ALCj,i (0≦i≦1023). If the LNL's=0 then the meaning islogical; if LNL=1, the meaning is numeric. If, for example the resultsof ten Terms are routed to the same GTL, and its threshold is 6, themeaning of its output is the truth value of the first order logicproposition “Six of these ten terms are present.” If TL=0, the truthvalue of the proposition “Six of these ten terms are present.” is false.If TL=1, the truth value of the proposition “Six of these ten terms arepresent.” is true. If the threshold is 1, the meaning of the TL value isequivalent to a disjunctive sum over the scan outcomes of the ten Terms,TL=1 means “At least one of the ten Terms is satisfied.” If thethreshold is 10, it has the meaning of the conjunctive sum over the scanoutcomes of the ten terms, that is, “All of the Terms are satisfied.”

If an individual Term routed to a GTL has its LNL=1 and the GTL has athreshold of n, the meaning of the TL is the truth value of “This Termhas been satisfied at least n times”.

INDUSTRIAL APPLICABILITY

The invention is further illustrated by the following non-limitingexamples:

-   -   1. Cybersecurity malware recognition—requires user-adjustable        acuity, high throughput and about $1000 per unit cost declining        to $10 per unit. Variety of malware signatures and diversity of        threat scenarios demands reference patterns of 1000 features or        greater.    -   2. Medical Therapy Design—GPSTX throughput and acuity will        enable finding patterns in DNA and associating them with        patterns of diseases, then with individual patient uptake        profiles to determine treatment protocols.    -   3. Terabyte scale Content Monitoring as in, e.g., patent        application examination or copyright infringement detection will        become economically attractive given GPSTX's profile of acuity,        throughput and cost.    -   4. Reusable Intellectual Assets—During design of systems and        products reusing existing, proven components saves time and        money and reduces introduction of errors. This has been well        known for decades but little practiced because locating suitable        reusable components is difficult. Typically any reusable object        has a critical mass of a dozen or more attributes many of which        are expressed in a lexicon different than the current designer        is using. Locating the few reusable objects in the milieu of        potentially reusable objects is the main challenge. The        equivalence class capability of the device described herein can        solve this problem.    -   5. Object oriented software accelerator—Computer software is        increasingly object-based because of the several advantages of        object technology. One disadvantage is that any one object        broadcasts messages that may be pertinent to any number of        (unpredictable) other objects. Present technology employs        sequential search to identify correspondent objects. When        upwards of 10**6 are active and each may emanate 10**2 to 10**4        messages per second the search for correspondents can become the        limiting factor in system performance. With a GPSTX co-processor        as described herein, the system execution time could be reduced        up to 10×.    -   6. Legal—beyond improving case law text search the device        enables the synthesis and analysis of scenarios in the discovery        and trial phases of litigation.    -   7. Business Intelligence and Military Intelligence depends on        high acuity recognition of factoids and situations. Further,        fusion of many seemingly disparate findings has been a key        problem that can now be solved with the equivalence class        capability of GPSTX.    -   8. Semantic Net Equivalence Brokerage.    -   9. Text search—Device can be used not only to locate characters,        strings, partial strings but also to locate contextual sets such        as sentences, paragraphs, sections, chapters, documents, etc.        relevant to a given issue and only those. To locate all        documents relevant to a given issue and only those documents.        Useful as Desktop, Workgroup Server, Enterprise portal, ISP and        Federated configurations.    -   10. Question Answering—current mode of FAQ can be enhanced by        improving content and structure of reference patterns through,        for example, interactive solicitation of user disambiguation.    -   11. Image, audio and steganography recognition

Although the invention has been described in detail with particularreference to these preferred embodiments, other embodiments can achievethe same results. Variations and modifications of the present inventionwill be obvious to those skilled in the art and it is intended to coverin the appended claims all such modifications and equivalents. Theentire disclosures of all references, applications, patents, andpublications cited above are hereby incorporated by reference.

What is claimed is:
 1. A method of detecting a bit pattern in a datastream, said bit pattern defined by a group of N bits, the methodcomprising: storing, during a load mode, a first binary value at a firstaddress of a memory, said address defined by the group of N bits;storing, during the load mode, a second binary value at remainingaddresses of the memory; providing a plurality of periods including asystolic cycle; retrieving, during each period, the stored binary valuefrom an address of the memory defined by a different one of N-bits ofthe data stream; and detecting the bit pattern using a detector cell ifthe binary value retrieved in response to an associated one of theN-bits of the data stream is the first binary value.
 2. The method ofclaim 1 wherein said first binary value is a binary one and said secondbinary value is a binary zero.
 3. The method of claim 2 wherein adecimal value of each of the N-bits of the data stream defines anaddress to be retrieved.
 4. The method of claim 1 wherein said bitpattern represents an alphanumeric character.
 5. The method of claim 1wherein said bit pattern represents a punctuation character.
 6. Themethod of claim 1 wherein said bit pattern represents a controlcharacter.
 7. The method of claim 1 wherein said bit pattern representsa space character.
 8. A method of detecting in a data stream a string ofM digital symbols, each digital symbol being represented by N bits, themethod comprising: providing M detector cells and associated patternmemory columns; storing, during a load mode, a first binary valuerepresenting a first logic state at each of a first address of the Mpattern memory columns each associated with one of the M digitalsymbols, wherein the first address of each of the M pattern memorycolumns is defined by a numerical value of the N bits of its associateddigital symbol; storing, during the load mode, a second binary value atremaining addresses of each of the M pattern memory columns; supplyingone of N-bits of the data stream as an address to a first one of the Mdetector cells and associated pattern memory columns; retrieving thedata stored in the address of the pattern memory column pointed to bythe N-bits of the data stream; maintaining the remaining M-1 detectorcells and associated pattern memory columns disabled; detecting a firstdigital symbol if the retrieved data has the first binary value;repeating the supplying and detecting steps until the retrieved data hasthe first binary value or until all N-bits of the data stream have beenaccounted for; enabling a second one of the M detector cells andassociated pattern memory columns if the first digital symbol isdetected; supplying another one of N-bits of the data stream as anaddress to a second one of the M detector cells and associated patternmemory columns; retrieving the data stored in the address of the secondpattern memory column pointed to by the other one of N-bits of the datastream; maintaining the remaining M-2 detector cells and associatedpattern memory columns disabled; repeating the supplying and detectingsteps until the retrieved data has the first binary value or until allremaining N-bits of the data stream have been accounted for; detecting asecond digital symbol if the data retrieved from the second patternmemory column has the first binary value.
 9. The method of claim 8wherein said digital symbol represents an alphanumeric character. 10.The method of claim 8 wherein said digital symbol represents apunctuation character.
 11. The method of claim 8 wherein said digitalsymbol represents a control character.
 12. The method of claim 8 whereinsaid digital symbol represents a gradation in a gray scale.
 13. A methodof performing pattern recognition, the method comprising: providing,during each period of a first plurality of periods, each periodincluding a systolic cycle, a different segment of a first portion of adata stream as an address to a pattern memory column having storedtherein a first binary value at a first address and a second binaryvalue at all other addresses; each data segment of the data streamrepresenting N successive bits; said first address defined by a digitalsymbol consisting of N successive bits; setting a bit if a data segmentin the first portion of the data stream defines the first address in thememory block; and detecting using a detector cell, the digital symbol inthe data segment if the bit is set.
 14. The method of claim 13 whereinsaid digital symbol is a first digital symbol of a string of M digitalsymbols, said M digital symbols undergoing pattern recognition, themethod further comprising: providing M detector cells and associatedpattern memory columns; enabling a second pattern memory column if thefirst digital symbol is detected; maintaining remaining (M-2) detectorcells and associated pattern memory columns disabled; providing, duringeach period of a second plurality of periods, each period including asystolic cycle, a different segment of a second portion of the datastream as an address to the second pattern memory column having storedtherein a first binary value at a first address and a second binaryvalue at all other addresses; setting a second bit if a data segment inthe second portion of the data stream defines the first address in thesecond pattern memory column; and detecting a second digital symbol inthe second portion of the data stream if the second bit is set.
 15. Themethod of claim 14 wherein said string of M digital symbols comprisesalphanumeric and white space characters.
 16. The method of claim 14wherein said digital symbol represents a punctuation character.
 17. Themethod of claim 14 wherein said digital symbol represents a controlcharacter.
 18. The method of claim 14 wherein said digital symbolrepresents a gradation in a gray scale.
 19. A method of identifying astring of M digital symbols in a data stream, each digital symbolrepresented by N bits, the method comprising: configuring a differentdetection cell with each digital symbol to establish an associationtherewith; each detection cell including an associated a pattern memorycolumn; enabling detection of kth digital symbol of the string in thedata stream only if all digital symbols preceding the kth digital symbolare detected in the data stream, wherein k is an integer varying from 1to M; disabling detection of (k+1 )th digital symbol of the string inthe data stream until the kth digital symbol of the string is detectedin the data stream, wherein the kth digital symbol in the data stream isdetected using the steps of: accessing in the associated pattern memorycolumn, an address defined by a numerical value of a segment of the datastream in which detection of the kth digital symbol is being made; anddetecting the kth digital symbol in the data segment if one or more bitsstored at the accessed address has a predefine value.
 20. The method ofclaim 19 wherein the string of digital symbols comprises alphanumericand white space characters.
 21. The method of claim 19 wherein thestring of digital symbols comprises punctuation characters.
 22. Themethod of claim 19 wherein the string of digital symbols comprisescontrol characters.
 23. The method of claim 19 wherein the string ofdigital symbols represents a gradation in a gray scale.
 24. The methodof claim 19 further comprising: storing a second binary value at eachaddress of each pattern memory column; said second binary valuecontrolling a state of a sequential logic block disposed in theassociated detection cell so as to enable or disable identification ofan associated digital symbol.
 25. The method of claim 24 furthercomprising: storing a third binary value at each address of each patternmemory column; said third binary value identifying whether the string ofdigital symbols is present in the data stream.
 26. The method of claim24 further comprising: associating a plurality of routing units witheach detection cell; using the routing cells associated with the kthdetection cell to enable the (k+1)th detection cell.
 27. The method ofclaim 26 wherein the routing units associated with each detection cellare disposed along a same column.
 28. The method of claim 26 wherein therouting unit associated with each detection cell are disposed along asame row.
 29. The method of claim 26 wherein the string of digitalsymbols comprises L terms each term comprising one or more alphanumericcharacters, each term separated from other terms by one or more whitespace characters.
 30. The method of claim 29 further comprising:defining one or more additional terms equivalent to at least one of theterms of disposed in the string of digital symbols; using a firstplurality of the routing units associated with different detection cellsto detect a beginning digital symbol of the one or more terms equivalentto the at least one term; and using a second plurality of the routingunits associated with the different the detection cells to detect anending digital symbol of the one or more terms equivalent to the atleast one term.
 31. The method of claim 30 wherein said first pluralityof the routing units are disposed along a first row and the detectioncells are disposed along a second row.
 32. The method of claim 31wherein said second plurality of the routing units are disposed along athird row.
 33. The method of claim 30 wherein said first plurality ofthe routing units are disposed along a first column and the detectioncells are disposed along a second column.
 34. The method of claim 33wherein said second plurality of the routing units are disposed along athird column.
 35. The method of claim 30 further comprising: bypassingone or more of the routing units if the one or more routing units aredetected as being defective.
 36. The method of claim 19 furthercomprising: disabling detection of a term of the string of digitalsymbols after the term is detected in the data stream once.
 37. Themethod of claim 19 further comprising: continuing to detect a term ofthe string of digital symbols in the data stream after the term isidentified in the data stream once.
 38. The method of claim 19 furthercomprising: maintaining a count of a number of terms of the string ofdigital symbols detected in the data stream.
 39. The method of claim 19further comprising: maintaining a count of a number of times a same termof the string of digital symbols is detected in the data stream.